Abstract
Inferring gene regulatory networks from expression data is difficult, but it is common and often useful. Most network problems are under-determined–there are more parameters than data points–and therefore data or parameter set reduction is often necessary. Correlation between variables in the model also contributes to confound network coefficient inference. In this paper, we present an algorithm that uses integrated, probabilistic clustering to ease the problems of under-determination and correlated variables within a fully Bayesian framework. Specifically, ours is a dynamic Bayesian network with integrated Gaussian mixture clustering, which we fit using variational Bayesian methods. We show, using public, simulated time-course data sets from the DREAM4 Challenge, that our algorithm outperforms non-clustering methods in many cases (7 out of 25) with fewer samples, rarely underperforming (1 out of 25), and often selects a non-clustering model if it better describes the data. Source code (GNU Octave) for BAyesian Clustering Over Networks (BACON) and sample data are available at: http://code.google.com/p/bacon-for-genetic-networks.
Highlights
Inferring gene regulatory networks from high-throughput gene expression data is a difficult task, in particular because of the high number of genes relative to the number of data points, and because of the random noise that is present in measurement
Though [3] have had success combining methods in order to infer genetic networks from different types of data simultaneously, here we focus on time-series data and the corresponding methods for network inference
We describe a fully Bayesian model of gene cluster interaction, and we demonstrate that probabilistic gene clustering in conjunction with a dynamic Bayesian network can aid in the inference of gene regulatory networks, even in the DREAM4 data sets, where no clusters were explicitly included
Summary
Inferring gene regulatory networks from high-throughput gene expression data is a difficult task, in particular because of the high number of genes relative to the number of data points, and because of the random noise that is present in measurement. This review directly compares several different types of approaches by summarizing the correctness of the genetic networks inferred from synthetic (in silico) data generated from a known network. Of particular interest are the results of each of the algorithms when applied to the DREAM4 In Silico Network Challenge data sets, which includes data types such as ‘‘knock-out’’, ‘‘knock-down’’, and timeseries data among the sub-challenges. Though [3] have had success combining methods in order to infer genetic networks from different types of data simultaneously, here we focus on time-series data and the corresponding methods for network inference. In the review of [7], two types of algorithms seem to outperform the others when applied to time-series data: dynamic Bayesian networks and causal structure identification (CSI) in non-linear dynamical systems (NDSs)
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